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Artigos de revistas sobre o assunto "Portfolio management Australia Econometric models"
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Yong, Jaime, e Anh Khoi Pham. "The long-term linkages between direct and indirect property in Australia". Journal of Property Investment & Finance 33, n.º 4 (6 de julho de 2015): 374–92. http://dx.doi.org/10.1108/jpif-01-2015-0005.
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Purpose– Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims to discuss these issues.Design/methodology/approach– The authors study the linkages between direct and indirect Australian property sectors from 1985 to 2013, with shares and bonds. This paper employs an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to de-smooth a valuation-based direct property index. The authors establish directional lead-lag relationships between markets using bi-variate Granger causality tests. Johansen cointegration tests are carried out to examine how direct and indirect property markets adjust to an equilibrium long-term relationship and short-term deviations from such a relationship with other asset classes.Findings– The authors find the use of appraisal-based property data creates a smoothing bias which masks the extent of how information is transmitted between the indirect property sector, stock and bond markets, and influences returns. The authors demonstrate that an ARFIMA process accounting for a smoothing bias up to lags of four quarters can overcome the overstatement of the smoothing bias from traditional AR models, after individually appraised constituent properties are aggregated into an overall index. The results show that direct property adjusts to information transmitted from market-traded A-REITs and stocks.Practical implications– The study shows direct property investments and A-REITs are substitutible in a multi-asset portfolio in the long and short term.Originality/value– The authors apply an ARFIMA(p,d,q) model to de-smooth Australian property returns, as proposed by Bond and Hwang (2007). The authors expect the findings will contribute to the discussion on whether direct property and REITs are substitutes in a multi-asset portfolio.
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Reddy, Wejendra. "Evaluation of Australian industry superannuation fund performance; asset allocation to property". Journal of Property Investment & Finance 34, n.º 4 (4 de julho de 2016): 301–20. http://dx.doi.org/10.1108/jpif-12-2015-0084.
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Purpose – Property is a key investment asset class that offers considerable benefits in a mixed-asset portfolio. Previous studies have concluded that property allocation should be within the 10-30 per cent range. However, there seems to be wide variation in theory and practice. Historical Australian superannuation data shows that the level of allocation to property asset class in institutional portfolios has remained constant in recent decades, restricted at 10 per cent or lower. This is seen by many in the property profession as a subjective measure and needs further investigation. The purpose of this paper is to compare the performance of the AU$431 billion industry superannuation funds’ strategic balanced portfolio against ten different passive and active investment strategies. Design/methodology/approach – The analysis used 20 years (1995-2015) of quarterly data covering seven benchmark asset classes, namely: Australian equities, international equities, Australian fixed income, international fixed income, property, cash and alternatives. The 11 different asset allocation models are constructed within the modern portfolio theory framework utilising Australian ten-year bonds as the risk free rate. The Sharpe ratio is used as the key risk-adjusted return performance measure. Findings – The ten different asset allocation models perform as well as the industry fund strategic approach. The empirical results show that there is scope to increase the property allocation level from its current 10-23 per cent. Upon excluding unconstrained strategies, the recommended allocation to property for industry funds is 19 per cent (12 per cent direct and 7 per cent listed). This high allocation is backed by improved risk-adjusted return performance. Research limitations/implications – The constrained optimal, tactical and dynamic models are limited to asset weight, no short selling and turnover parameters. Other institutional constraints that can be added to the portfolio optimisation problem include transaction costs, taxation, liquidity and tracking error constraints. Practical implications – The 11 different asset allocation models developed to evaluate the property allocation component in industry superannuation funds portfolio will attract fund managers to explore alternative strategies (passive and active) where risk-adjusted returns can be improved, compared to the common strategic approach with increased allocation to property assets. Originality/value – The research presents a unique perspective of investigating the optimal allocation to property assets within the context of active investment strategies, such as tactical and dynamic models, whereas previous studies have focused mainly on passive investment strategies. The investigation of these models effectively contributes to the transfer of broader finance and investment market theories and practice to the property discipline.
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Shah, Rohan, e Phani R. Jammalamadaka. "Optimal Portfolio Strategy for Risk Management in Toll Road Forecasts and Investments". Transportation Research Record: Journal of the Transportation Research Board 2670, n.º 1 (janeiro de 2017): 83–94. http://dx.doi.org/10.3141/2670-11.
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The study leveraged modern portfolio theory and stochastic time series models to develop a risk management strategy for future traffic projections along brownfield toll facilities. Uncertainty in future traffic forecasts may raise concerns about performance reliability and revenue potential. Historical time series traffic data from brownfield corridors were used for developing econometric forecast estimates, and Monte Carlo simulation was used to quantify a priori risks or variance to develop optimal forecasts by using mean-variance optimization strategies. Numerical analysis is presented with historical toll transactions along the Massachusetts Turnpike system. Suggested diversification strategies were found to achieve better long-term forecast efficiencies with improved trade-offs between anticipated risks and returns. Planner and agency forecast performance expectations and risk propensity are thus jointly captured.
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Brdyś, Mietek A., Marcin T. Brdyś e Sebastian M. Maciejewski. "Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations". International Journal of Applied Mathematics and Computer Science 26, n.º 1 (1 de março de 2016): 161–73. http://dx.doi.org/10.1515/amcs-2016-0011.
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Abstract The paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.
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Ogorelkova, Natalya Vladimirovna, e Irina Mikhaylovna Reutova. "FACTORS OF THE EFFICIENCY OF MANAGING PORTFOLIO PENSION RESERVES OF NON-STATE PENSION FUNDS". Scientific Bulletin: finance, banking, investment., n.º 3 (52) (2021): 22–30. http://dx.doi.org/10.37279/2312-5330-2020-3-22-30.
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The article is devoted to the consideration of approaches and assessment of the efficiency of management of investment portfolios of non-state pension funds. This article is a logical continuation of the previously conducted research on assessing the effectiveness of pension savings management and contains an analysis of the effectiveness of the second component of investment portfolios of non-state pension funds (NPF) — pension reserves. The article examines the factors influencing the efficiency of managing the portfolios of pension reserves of non-state pension funds on the basis of statistical data on 28 NPFs for 2013–2018. The factors chosen were the volumes and growth rates of the funds attracted from pension reserves, the share of pension reserves in the economies of scale of non-state pension funds, the presence of risk strategies (the share of shares and investment units), and the amount of remuneration of management companies. The aim of the study is to assess the influence of the selected factors on the efficiency of managing the portfolio of pension reserves of NPFs based on the construction of econometric models. The construction of one-factor and multi-factor econometric models confirms the absence of dependence of the effectiveness of portfolios of pension reserves of APFs, determined by the Sharpe ratio, on the size of attracted pension reserves per one insured person; from the share occupied by NPFs in the non-state pension market, as well as from remuneration to management companies paid by non-state pension funds. The influence of the chosen investment strategy and the growth rate of pension reserves on the efficiency of managing pension reserves of NPFs is revealed.
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Kucukkocaoglu, Guray, e M. Ayhan Altintas. "Using non-performing loan ratios as default rates in the estimation of credit losses and macroeconomic credit risk stress testing: A case from Turkey". Risk Governance and Control: Financial Markets and Institutions 6, n.º 1 (2016): 52–63. http://dx.doi.org/10.22495/rgcv6i1art6.
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In this study, inspired by the Credit Portfolio View approach, we intend to develop an econometric credit risk model to estimate credit loss distributions of Turkish Banking System under baseline and stress macro scenarios, by substituting default rates with non-performing loan (NPL) ratios. Since customer number based historical default rates are not available for the whole Turkish banking system’s credit portfolio, we used NPL ratios as dependent variable instead of default rates, a common practice for many countries where historical default rates are not available. Although, there are many problems in using NPL ratios as default rates such as underestimating portfolio losses as a result of totally non-homogeneous total credit portfolios and transferring non-performing loans to asset management companies from banks’ balance sheets, our aim is to underline and limit some ignored problems using accounting based NPL ratios as default rates in macroeconomic credit risk modeling. Developed models confirm the strong statistical relationship between systematic component of credit risk and macroeconomic variables in Turkey. Stress test results also are compatible with the past experiences.
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Zagaglia, Paolo. "International diversification for portfolios of European fixed-income mutual funds". Managerial Finance 43, n.º 2 (13 de fevereiro de 2017): 242–62. http://dx.doi.org/10.1108/mf-01-2015-0026.
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Purpose The purpose of this paper is to study the scope for country diversification in international portfolios of mutual funds for the “core” EMU countries. The author uses a sample of daily returns for country indices of French, German and Italian funds to investigate the quest for international diversification. The author focuses on fixed-income mutual funds during the period of the financial market turmoil since 2007. Design/methodology/approach The author compute optimal portfolio allocations from both unconstrained and constrained mean-variance frameworks that take as input the out-of-sample forecasts for the conditional mean, volatility and correlation of country-level indices for funds returns. The author also applies a portfolio allocation model based on utility maximization with learning about the time-varying conditional moments. The author compares the out-of-sample forecasting performance of 12 multivariate volatility models. Findings The author finds that there is a “core” EMU country also for the mutual fund industry: optimal portfolios allocate the largest portfolio weight to German funds, with Italian funds assigned a lower weight in comparison to French funds. This result is remarkably robust across competing forecasting models and optimal allocation strategies. It is also consistent with the findings from a utility-maximization model that incorporates learning about time-varying conditional moments. Originality/value This is the first study on optimal country-level diversification for a mutual fund investor focused on European countries in the fixed-income space for the turmoil period. The author uses a large array of econometric models that captures the salient features of a period characterized by large changes in volatility and correlation, and compare the performance of different optimal asset allocation models.
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Jacobs Jr., Michael. "Supervisory requirements and expectations for portfolio level counterparty credit risk measurement and management". Journal of Financial Regulation and Compliance 22, n.º 3 (8 de julho de 2014): 252–70. http://dx.doi.org/10.1108/jfrc-10-2013-0034.
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Purpose – This study aims to survey supervisory requirements and expectations for counterparty credit risk (CCR). Design/methodology/approach – In this paper, a survey of CCR including the following elements has been performed. First, various concepts in CCR measurement and management, including prevalent practices, definitions and conceptual issues have been introduced. Then, various supervisory requirements and expectations with respect to CCR have been summarized. This study has multiple areas of relevance and may be extended in various ways. Risk managers, traders and regulators may find this to be a valuable reference. Directions for future research could include empirical analysis, development of a theoretical framework and a comparative analysis of systems for analyzing and regulating CCR. Findings – Some of the thoughts regarding the concept of risk will be considered and surveyed, and then how these apply to CCR will be considered. A classical dichotomy exists in the literature, the earliest exposition upon which is credited to Knight (1921), who defines uncertainty is when it is not possible to measure a probability distribution or it is unknown. This is contrasted with the situation where either the probability distribution is known, or knowable through repeated experimentation. Arguably, in economic and finance (and more broadly in the social or natural as opposed to the physical or mathematical sciences), the former is a more realistic scenario that is being contending with (e.g. a fair vs loaded die, or die with unknown number of sides.) The authors are forced to rely upon empirical data to estimate loss distributions, but this is complicated because of changing economic conditions, which invalidate forecasts that our econometric models generate. Originality/value – This is one of few studies of the CCR regulations that is so far-reaching.
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Shirur, Srinivas. "Are Managers Measuring the Financial Risk in the Right Manner? An Exploratory Study". Vikalpa: The Journal for Decision Makers 38, n.º 2 (abril de 2013): 81–94. http://dx.doi.org/10.1177/0256090920130205.
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The basic problem with corporate finance is that it deals with the fundamental analysis issues while the tools used are those applicable for technical analysis. That is the reason why finance managers often arrive at wrong decisions which snowball into issues like the subprime crisis. Initially, Markowitz model was used to calculate risk for portfolio management. It gave importance only to systematic risk as unsystematic risk could be avoided through diversification. Later on, CAPM model was developed for corporate finance and project finance for calculation of risk. Finance models dealing with risk management are applicable only for a short period and that too for an average of a large number of companies. The approach to apply risk measurement technique suitable for portfolio management to corporate finance is not correct. Even the econometric techniques applied to validate calculation of risk for portfolio management should be different from those applied for corporate finance. The present article analyses the problems of applying such risk measurement techniques for corporate finance purpose. A company faces mainly two types of risks: liquidity risk and bankruptcy risk. In case a company suffers from bankruptcy threat (which may or may not lead to actual bankruptcy), i.e., possibilities of closure due to losses, there will be two possibilities: The company may move with market index in normal times while it may come down suddenly with index and may not bounce back (Kink in the beta curve), as in the case of MTNL and Jet Airlines. There may be a sudden bankruptcy threat as in the case of Satyam. The latter case does not allow investors to react. However, corporate managers will have to take account of the first possibility of bankruptcy risk which cannot be ignored by assuming beta to be constant. This paper examines three companies, Mastek, Jet Airlines, and MTNL, in this category. The author suggests that instead of segregating risk into systematic and unsystematic risk, it should be segregated into bankruptcy and liquidity risk. In this way, unsystematic risk is also priced while determining the value of a company.
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Duppati, Geeta, e Mengying Zhu. "Oil prices changes and volatility in sector stock returns: Evidence from Australia, New Zealand, China, Germany and Norway". Corporate Ownership and Control 13, n.º 2 (2016): 351–70. http://dx.doi.org/10.22495/cocv13i2clp4.
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The paper examines the exposure of sectoral stock returns to oil price changes in Australia, China, Germany, New Zealand and Norway over the period 2000-2015 using weekly data drawn from DataStream. The issue of volatility has important implications for the theory of finance and as is well-known accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management (e.g. in the calculation of hedge ratios and Value-at-Risk measures), and trading strategies (David and Ruiz, 2009). This study adopts GARCH and EGARCH to understand the relationship between the returns and volatility. The findings using GARCH (EGARCH) models suggests that in the case of Germany eight (nine) out of ten sectors returns can be explained by the volatility of past oil price in Germany, while in the case of Australia, six (seven) out of ten sector returns are sensitive to the oil price changes with the exception of Industrials, Consumer Goods, Health care and Utilities. While in China and New Zealand five sectors are found sensitive to oil price changes and three sectors in Norway, namely Oil & Gas, Consumer Services and Financials. Secondly, this paper also investigated the exposure of the stock returns to oil price changes using market index data as a proxy using GARCH or EGARCH model. The results indicated that the stock returns are sensitive to the oil price changes and have leverage effects for all the five countries. Further, the findings also suggests that sector with more constituents is likely to have leverage effects and vice versa. The results have implications to market participants to make informed decisions about a better portfolio diversification for minimizing risk and adding value to the stocks.
Teses / dissertações sobre o assunto "Portfolio management Australia Econometric models"
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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction". Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.
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Limkriangkrai, Manapon. "An empirical investigation of asset-pricing models in Australia". University of Western Australia. Faculty of Business, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0197.
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[Truncated abstract] This thesis examines competing asset-pricing models in Australia with the goal of establishing the model which best explains cross-sectional stock returns. The research employs Australian equity data over the period 1980-2001, with the major analyses covering the more recent period 1990-2001. The study first documents that existing asset-pricing models namely the capital asset pricing model (CAPM) and domestic Fama-French three-factor model fail to meet the widely applied Merton?s zero-intercept criterion for a well-specified pricing model. This study instead documents that the US three-factor model provides the best description of Australian stock returns. The three US Fama-French factors are statistically significant for the majority of portfolios consisting of large stocks. However, no significant coefficients are found for portfolios in the smallest size quintile. This result initially suggests that the largest firms in the Australian market are globally integrated with the US market while the smallest firms are not. Therefore, the evidence at this point implies domestic segmentation in the Australian market. This is an unsatisfying outcome, considering that the goal of this research is to establish the pricing model that best describes portfolio returns. Given pervasive evidence that liquidity is strongly related to stock returns, the second part of the major analyses derives and incorporates this potentially priced factor to the specified pricing models ... This study also introduces a methodology for individual security analysis, which implements the portfolio analysis, in this part of analyses. The technique makes use of visual impressions conveyed by the histogram plots of coefficients' p-values. A statistically significant coefficient will have its p-values concentrated at below a 5% level of significance; a histogram of p-values will not have a uniform distribution ... The final stage of this study employs daily return data as an examination of what is indeed the best pricing model as well as to provide a robustness check on monthly return results. The daily result indicates that all three US Fama-French factors, namely the US market, size and book-to-market factors as well as LIQT are statistically significant, while the Australian three-factor model only exhibits one significant market factor. This study has discovered that it is in fact the US three-factor model with LIQT and not the domestic model, which qualifies for the criterion of a well-specified asset-pricing model and that it best describes Australian stock returns.
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Milunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series". Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.
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This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this
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Chen, Hongqing. "An Empirical Study on the Jump-diffusion Two-beta Asset Pricing Model". PDXScholar, 1996. https://pdxscholar.library.pdx.edu/open_access_etds/1325.
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This dissertation focuses on testing and exploring the usage of the jump-diffusion two-beta asset pricing model. Daily and monthly security returns from both NYSE and AMEX are employed to form various samples for the empirical study. The maximum likelihood estimation is employed to estimate parameters of the jump-diffusion processes. A thorough study on the existence of jump-diffusion processes is carried out with the likelihood ratio test. The probability of existence of the jump process is introduced as an indicator of "switching" between the diffusion process and the jump process. This new empirical method marks a contribution to future studies on the jump-diffusion process. It also makes the jump-diffusion two-beta asset pricing model operational for financial analyses. Hypothesis tests focus on the specifications of the new model as well as the distinction between it and the conventional capital asset pricing model. Both parametric and non-parametric tests are carried out in this study. Comparing with previous models on the risk-return relationship, such as the capital asset pricing model, the arbitrage pricing theory and various multi-factor models, the jump-diffusion two-beta asset pricing model is simple and intuitive. It possesses more explanatory power when the jump process is dominant. This characteristic makes it a better model in explaining the January effect. Extra effort is put in the study of the January Effect due to the importance of the phenomenon. Empirical findings from this study agree with the model in that the systematic risk of an asset is the weighted average of both jump and diffusion betas. It is also found that the systematic risk of the conventional CAPM does not equal the weighted average of jump and diffusion betas.
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Hakim, Abdul. "Modelling the interactions across international stock, bond and foreign exchange markets". UWA Business School, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0202.
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[Truncated abstract] Given the theoretical and historical evidence that support the benefit of investing internationally. there is Iittle knowledge available of proper international portfolio construction in terms of how much should be invested in foreign countries, which countries should be targeted, and types of assets to be included in the portfolio. The prospects of these benefits depend on the market volatilities, cross-country correlations, and currency risks to change in the future. Another important issue in international portfolio diversification is the growth of newly emerging markets which have different characteristics from the developed ones. Addressing the issues, the thesis intends to investigate the nature of volatility, conditional correlations, and the impact of currency risks in international portfolio, both in developed and emerging markets. Chapter 2 provides literature review on volatility spillovers, conditional correlations, and forecasting both VaR and conditional correlations using GARCH-type models. Attention is made on the estimated models, type of assets, regions of markets, and tests of forecasts. Chapter 3 investigates the nature of volatility spillovers across intemational assets, which is important in determining the nature of portfolio's volatility when most assets are seems to be connected. ... The impacts of incorporating volatility spillovers and asymmetric effect on the forecast performance of conditional correlation will also be examined in this thesis. The VARMA-AGARCH of McAleer, Hoti and Chan (2008) and the VARMA-GARCH model of Ling and McAleer (2003) will be estimated to accommodate volatility spillovers and asymmetric effect. The CCC model of Bollerslev (1990) will also be estimated as benchmark as the model does not incorporate both volatility spillovers and asymmetric effects. Given the information about the nature of conditional correlations resulted from the forecasts using a rolling window technique, Section 2 of Chapter 4 investigates the nature of conditional correlations by estimating two multivariate GARCH models allowing for time-varying conditional correlations, namely the DCC model of Engle (2002) and the GARCC model of McAleer et al. (2008). Chapter 5 conducts VaR forecast considering the important role of VaR as a standard tool for risk management. Especially, the chapter investigates whether volatility spillovers and time-varying conditional correlations discussed in the previous two chapters are of helps in providing better VaR forecasts. The BEKK model of Engle and Kroner (1995) and the DCC model of Engle (2002) will be estimated to incorporate volatility spillovers and conditional correlations, respectively. The DVEC model of Bollerslev et al. (1998) and the CCC model of Bollerslev (1990) will be estimated to serve benchmarks, as both models do not incorporate both volatility spillovers and timevarying conditional correlations. Chapter 6 concludes the thesis and lists somc possible future research.
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"Multi-period optimal portfolio selection with limited rebalancing opportunities". 2011. http://library.cuhk.edu.hk/record=b5894622.
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Wang, Yang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 72-74).
Abstracts in English and Chinese.
Chapter 1 --- Literature Review and Model Description --- p.1
Chapter 1.1 --- Portfolio theory under mean-variance framework --- p.2
Chapter 1.2 --- Portfolio theory under utility-maximizing framework --- p.5
Chapter 1.3 --- Model Description --- p.11
Chapter 2 --- Parameterized optimal rebalancing strategy --- p.14
Chapter 2.1 --- An open-loop policy of the T-horizon model --- p.16
Chapter 2.2 --- A closed-loop policy of the T-horizon model --- p.24
Chapter 2.3 --- Illustrative numerical example --- p.36
Chapter 3 --- Non-parameterized optimal rebalancing model --- p.46
Chapter 3.1 --- T=2 period problem --- p.47
Chapter 3.2 --- T=3 period problem --- p.55
Chapter 4 --- s-S type policy --- p.59
Chapter 4.1 --- Exponential K-convex function --- p.60
Chapter 4.2 --- Revised multiperiod portfolio selection model --- p.62
Chapter 5 --- Conclusion and summary of work --- p.70
Bibliography --- p.71
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"Exploit market abnormal return using data mining with application to optimal portfolio selection". 2004. http://library.cuhk.edu.hk/record=b5892005.
Texto completo da fonteResumo:
Tsui Chuk Wah.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 69-70).
Abstracts in English and Chinese.
Abstract --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Data --- p.8
Chapter 3 --- Methodology --- p.23
Chapter 4 --- Results --- p.45
Chapter 5 --- Conclusion and Further Development --- p.59
Appendix --- p.63
Reference --- p.69
Livros sobre o assunto "Portfolio management Australia Econometric models"
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Clark, Francis Jack, e Francis Jack Clark, eds. Portfolio analysis. 3a ed. Englewood Cliffs, N.J: Prentice-Hall, 1986.
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Brandt, Michael W. Dynamic portfolio selection by augmenting the asset space. Cambridge, MA: National Bureau of Economic Research, 2004.
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Mohan, S. R. Portfolio selection for management of foreign exchange reserves. Mumbai: Dept. of Economic Analysis and Policy, Reserve Bank of India, 1993.
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Jurek, Jakub W. Optimal value and growth tilts in long-horizon portfolios. Cambridge, Mass: National Bureau of Economic Research, 2006.
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Mohan, S. R. Portfolio selection for management of foreign exchange reserves. Mumbai: Dept. of Economic Analysis and Policy, Reserve Bank of India, 1993.
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Mohan, S. R. Portfolio selection for management of foreign exchange reserves. Mumbai: Dept. of Economic Analysis and Policy, Reserve Bank of India, 1993.
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Dovalee, Dorsett, e Institute of Chartered Financial Analysts. Research Foundation., eds. Time diversification revisited. [Charlottesville, Va.]: Research Foundation of the Institute of Chartered Financial Analysts, 1995.
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Liu, Jun. Dynamic asset allocation with event risk. Cambridge, MA: National Bureau of Economic Research, 2002.
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Personal sector expenditure and portfolio decisions: An integrated model. Aldershot, Hants, England: Avebury, 1991.
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Satchell, Stephen E. A demystification of the Black-Littermann model: Managing quantitative and traditional portfolio construction. Cambridge: Judge Institute of Management Studies, University of Cambridge, 1997.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Portfolio management Australia Econometric models"
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"Econometric Models". In Active Credit Portfolio Management in Practice, 182–253. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118266830.ch4.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Portfolio management Australia Econometric models"
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Dobrina, Maria V., Yana A. Yurova e Galina V. Shurshikova. "Econometric Models with Discrete Dependent Variable in Portfolio Analysis". In Proceedings of the 2nd International Conference on Economy, Management and Entrepreneurship (ICOEME 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoeme-19.2019.18.
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